Summary: Maternal mortality rates in the United States are far exceeding those of other developed nations. This healthcare crisis is disproportionately affecting marginalized populations, including Black women and women living in rural communities. The MEND framework-developed through extensive research and informed by real-world case studies-offers a comprehensive and equity-driven solution to address existing disparities and improve maternal health outcomes nationwide. This framework emphasizes four objectives (or pillars), named after the first letter of each pillar: (1) Maternal care integration, (2) Equity-driven policies, (3) Navigated support, and (4) Driving collaboration. Each pillar presents actionable strategies to reduce maternal mortality and morbidity in the United States. The MEND framework offers a comprehensive, scalable roadmap for systemic change to help ensure equitable, high-quality care for all mothers.
Goal: The purpose of the research is to explore, through the lens of organizational performance and staff satisfaction, the characteristics of administrative leaders working as dyad partners with physician leaders.
Methods: All 54 administrative leaders from 71 clinical departments at the three US Mayo Clinic sites were invited to participate in the study. We used an unsupervised cluster analysis machine learning method to group the leaders based on their characteristics, as measured by the 32-dimension Occupational Personality Questionnaire (OPQ 32r), and we used a three-cluster model to explore the relationships between the clusters and the performance outcome. We took the department performance data from the previous year and compared the percentage of departments with the upward changes among the clusters. For staff survey data, we calculated the percentage of departments with scores that were above average among the three clusters for both physician and administrative staff responses.
Principal findings: Analysis of personality data revealed three different clusters. Cluster 1 leaders were caring and democratic, forward-thinking, strategic, optimistic, and trusting of others. Cluster 2 leaders were extremely hardworking and authoritative. Cluster 3 leaders were caring, modest, and rule-following. Cluster 1 leaders showed the best financial performance and sense of belonging among their followers, cluster 2 leaders elicited high engagement from their departments, and cluster 3 leaders encouraged lower burnout among staff members.
Practical applications: From this study, we obtained empirical evidence of administrative leaders' characteristics that showed positive relationships with financial and staff-satisfaction metrics. The results showed that distinct types of leaders influence administrative staff and physician staff differently and that different situations require different styles of leadership. We can also conclude that implementing robust, scientifically validated tools to assess leadership traits and tendencies can positively affect leadership and organizational performance for healthcare organizations.
Goal: Despite the well-documented mental health impact of the COVID-19 pandemic on healthcare workers (HCWs), the literature holds limited research on their use of mental healthcare. This study assessed the prevalence and correlates of mental healthcare utilization among US HCWs, which can be used as baseline measurements to guide the evaluation of interventions and guide the development of those interventions.
Methods: We used the 2020-2021 US National Health Interview Survey and restricted our analytic sample to respondents who worked in healthcare settings and reported daily, weekly, or monthly mental health symptoms (unweighted n = 1,412). Our outcome variables were: (1) receiving anxiolytic or antidepressant prescriptions, (2) receiving psychotherapy, and (3) not utilizing either treatment. We conducted multivariable logistic regression models to identify factors associated with each outcome. Based on Andersen's behavioral model, we included predisposing factors (e.g., gender, healthcare role), enabling factors (e.g., social support, telehealth use), need factors (e.g., frequency of depressive or anxiety symptoms), and year.
Principal findings: We found that 32.1% of HCWs received prescriptions, 22.3% received psychotherapy, and 59.0% were not currently using mental healthcare. Overall, some predisposing, enabling, and need factors were associated with all three outcome variables for mental healthcare utilization among HCWs. For instance, when examining the odds of not reporting current use of mental healthcare services, odds were higher among HCWs who were non-Hispanic Black/African American (odds ratio [OR] = 1.90, 95% confidence interval [CI] [1.16-3.12]), or Hispanic (OR = 2.68, 95% CI [1.63-4.39]) compared to those who were non-Hispanic White. Higher odds were also observed among HCWs who reported rarely or never received adequate social support (OR = 1.94, 95% CI [1.04-3.62]) as compared to those who reported always receiving adequate social support, those who were male (OR = 1.47, 95% CI [1.00-2.16]), and those without a usual source of care (OR = 2.08, 95% CI [1.12-3.88]). Inversely, lower odds were observed among HCWs who reported themselves as not heterosexual (OR = 0.58, 95% CI [0.34-0.99]) and those who had used telehealth appointments (OR = 0.32, 95% CI [0.24-0.44]). Lower odds were also observed among HCWs with more frequent anxiety symptoms: monthly (OR = 0.42, 95% CI [0.20-0.88]), weekly (OR = 0.36, 95% CI [0.18-0.73]), or daily frequency (OR = 0.27, 95% CI [0.14-0.55]), compared to never or few times a year. A similar pattern was observed among HCWs with more frequent depressive symptoms: monthly (OR = 0.33, 95% CI [0.22-0.49]), weekly (OR = 0.15, 95% CI [0.09-0.24]), or daily (OR = 0.11, 95% CI [0.05-0.21]), compared to never or few times a year. No differences in any outcome variable by type of HCW (diagnosing vs. nondiagnosing roles) were observed.
Healthcare administrators have historically accepted patient-level information asymmetry as an unavoidable complication of healthcare delivery, addressing it primarily through policy intervention and improved educational materials. This essay presents an innovative strategy leveraging artificial intelligence (AI) to bridge this communication gap. Beginning with an analysis of asymmetry's impact on healthcare delivery, the discussion examines how emerging AI capabilities could transform patient education and provider communication. The growing adoption of telehealth services demonstrates an increasingly tech-savvy patient population receptive to digital healthcare solutions. This essay also addresses implementation concerns, including technical infrastructure requirements, and provides recommendations for overcoming these challenges. Finally, a cost-benefit analysis examines initial investment requirements and projected organizational savings, offering healthcare administrators a framework for evaluating a technological solution to persistent information asymmetry in healthcare.
Goal: This study aimed to determine whether patients who identify as Black/African-American or Hispanic/Latino have different expectations for and experiences of therapeutic connections (TCs) with care providers, compared to those who identify as non-Hispanic White. Although race-based health disparities have been recognized in the United States for decades, efforts to reduce them have yielded inconsistent results. Early evidence suggests that high-quality TCs have important impacts on patient outcomes, which could help explain the persistence of certain disparities.
Methods: Primary data were collected during a field study that recruited patients from across the U.S. (N = 1,598). We used a cross-sectional online survey of non-Hispanic Black, non-Hispanic White, and Hispanic or Latino (any race) adults who had a healthcare encounter in the previous six months. The sampling strategy oversampled Black and Hispanic/Latino patients and balanced respondents across age groups. The survey asked respondents questions about their expectations for ideal TCs, TC experiences, and satisfaction with their main care provider. Our large sample enabled subgroup analyses that examined the experiences of those with certain intersectional identities (e.g., race and gender). Variables were examined using omnibus analysis of variance with Fisher's least significant difference post hoc tests to compare specific groups.
Principal findings: There were no differences between groups regarding their expectations for ideal TCs. There were, however, differences by race/ethnicity in TC experiences and satisfaction. Differences were more prevalent in subgroup analyses. Chronic conditions, gender, and racial concordance with the provider mattered for some measures but not for others. Generally, Hispanic or Latino patients reported significantly lower levels of experienced TCs.
Practical applications: Understanding the differences in experiences of care and patient satisfaction by race/ethnicity can facilitate the cultivation of targeted interventions and policies aimed at addressing disparities in healthcare delivery and further promote equitable care for all patients. Nevertheless, more must be done to understand what might lead to poorer TCs for some who identify with marginalized groups and whether poorer TCs lead to poorer health outcomes.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：